EllipScape: A Genetic Algorithm Based Approach to Non-Photorealistic Colored Image Reconstruction for Evolutionary Art
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Date
2024-01-03
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1774
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Abstract
The image reconstruction problem has uses in several areas, including the medical field, resolution scaling, and art. The goal of image reconstruction is to recreate an image as close to the original image as possible. While in many cases this goal is to nearly match a target image (or, in the case of generative machine learning problems, to match an image to a target group), it can be useful to examine methods of recreating an image with imperfections, often for the purposes of artistic expression or an understanding of the underlying structure of the image being reconstructed. In this paper, we introduce EllipScape -- a colored, non-photorealistic image reconstruction algorithm utilizing a genetic algorithm. This algorithm produces a resulting image that is similar to the original image but is created from ellipses of different sizes and colors. We show that the performance of this algorithm scales well and executes in a reasonable amount of time for an arbitrarily sized image.
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Soft Computing: Theory Innovations and Problem-Solving Benefits, evolutionary art, image reconstruction, non-photorealistic images
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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